Hi, dear readers! I’m Sara Vaca (independent consultant and Data Visualization lover, Saturday eventual contributor to this blog) and I thought of starting the year by sharing tips about something people sometimes ask me.
Leason Learned: Becoming slightly obsessed with something helps.
I discovered Dataviz in 2011 while I started on Twitter, during a maternity long leave. I remember seeing the first hashtagged infographics there (of course I had seen visuals before but no one had called them that) and remember thinking: “I need to learn this (visual) language”. And that day, my mini-obsession began.
Cool Tips: So what did I do? Here are my tips:
1) I started reading about Dataviz in blogs of the experts that generously were writing about it. My first favorite was Alberto Cairo, but there were also of course the late Hans Rosling, controversial David McCandless whose informationisbeautiful’s style always inspired me, Andy Kirk with his huge repository visualisingdata, then I discovered Ann Emery and Stephanie Evergreen who were already combining dataviz with evaluation, and I got even more excited.
2) Also I started watching TED talks and conferences.
3) Then I started trying to think visually, like an unconscious riddle to my mind: which things that we usually represent with words and paragraphs could be presented differently -like visually?
4)And I started playing, literally.
A friend told me that you have to dedicate time to things you love, but also, to things that disturb you. So first thing I started playing with was CVs (as it disturbed me how dull they usually look). I did mine and many friends’, and I started offering people to collaborate with them, trying to introduce visual elements in their reports or books (with little success in the beginning, either in response or in final results, but always fun).
In 2012 I started my M.A. in Evaluation and I decided to try to do all my homework visually (again a playful way of practicing – some examples: What is evaluation, History of Evaluation, Evolution of paradigms, amongst many others).
Note: By that time, most of the visuals I was creating were hideous and/or ineffective.
5) In 2013 I started conducting evaluations myself, so of course, I began creating visual elements for my reports.
6) By the same time, I started to blog to gently force myself to think, to practice, to produce more visuals. At the beginning I used to be very exigent with the level of the visual posted, but then I realized that often is not only what you share but also what it triggers into other people’s thoughts. So any idea is almost welcome now – and I have sooooo many!
7) And I’ve never taken any training course, but of course there are many in-person and online.
Lastly, it’s true that in 2012 I started learning basic Illustrator on my own, but trust me: your brain, paper and Powerpoint is honestly all you need to start.
All the best on your visual journey 🙂
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Hi Sara,
You have discussed an important point of visualization in your post. I agree that voiding visual language or absence of visual words makes writing quite boring and dull. I also agree that with visual language, it is easy to communicate the desired message. It is also evident that learning a visual language requires a lot of hard work. Though, evaluators can be trained on use of visuals and visual language through appropriate training programs and you shared the ways how it can be done.
The evaluation of any program is generally a combination of quantitative or qualitative data. Qualitative research generally encompasses open ended questions and the responses have a huge variety in it. To derive the true meaning of these responses, we require an appropriate coding and decoding of the qualitative responses. Once, these responses are decoded uniformly, the next step is to match the visuals of these responses.
As a layman in the field of evaluation, I feel that coding and decoding of exact responses is an uphill task while creating standard visuals is another challenge as visuals are based on individual perception. As human perception remains unique, hence, there remains a big challenge to derive standard visuals. How can we bridge this gap as evaluators of creating standardized visuals? Moreover, if we generate standardized visuals from the collected data, how it can stop biased approach.
Thank you
Salman
Hi Salman, thanks for your comment. To be honest, I hardly ever use standarized visuals for qualitative data. I work on them trying to visualize what the text (conclusion, summary, idea) is telling me. However, getting rid of the bias present in the conclusions of the responses is another (lost) battle, and I also assume my visuals have my personal filter, therefore its own bias. But still worth trying and in my opinion more powerful for understanding, analyzing and communicating than working with just text. Thanks!
This is the article I have been waiting for! Thank you so so much. I first came across data visualisation while working at the Office for National Statistics, where they have a small but brilliant datavis team. I was so impressed with their work but never even knew where to begin. I will now be working my way through your tips, thank you!
Great to hear, Esther! 🙂